Abstract: In this paper, we study the stochastic state trajectory and conductance distributions of memristors under periodic pulse excitation. Our results, backed by experimental evidence, reveal that ...

Abstract: In this paper, we propose a novel adaptive importance sampling (AIS) algorithm for probabilistic inference. The sampler learns a proposal distribution adaptation strategy by framing AIS as a ...

The behavior of language models is influenced by the prior context provided in prompts. Depending on whether you pick synthesis or shake, the next row looks very different — Vishal Misra Contextual ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Perceptual judgments of ambiguous stimuli are often biased by prior expectations. These biases may offer a window into the neural computations that give rise to perceptual interpretations of the ...

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...

Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of ...

Inference of gene flow using genomic data requires powerful methods as the process of coalescent, migration, and mutation is highly stochastic. However, it is challenging to implement the multispecies ...

Don’t worry, a little Bayesian analysis won’t hurt you. By Siobhan Roberts There is a statistician’s rejoinder — sometimes offered as wry criticism, sometimes as honest advice — that could hardly be a ...

Bayesian Statistics for Beginners. A Step-by-Step Approach (Donovan and Mickey, 2019) is, perhaps, the “truest-to-title” book I have read on Bayesian inference and statistics, insofar (a) it is ...